The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
Open accessAbstract
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.
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Segment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536
“…cover a wide range of object types, such as brain organs and tumors [29], [35], [42], [47], [66], lungs and hearts [23], [61], [64], [71], abdomen [22], [24], [57], [63], [66], [81], spine [65], [77], [78], cells [32], and polyps [38], [59], etc. Table I provides a detailed list of the collected…”
Self-citationCLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01934 · Cited by 333
Self-citationUniverSeg: Universal Medical Image Segmentation
Butoi, V. I., Gonzalez Ortiz, J. J., Ma, T., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01960 · Cited by 195
Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence
Anghel, C., Mugur, G. C., Anghel, D. A., Rusu-Munteanu, G. I., Dumitru, R. L., & Lupescu, I. G. (2024) · doi:10.20944/preprints202401.0904.v1 · Cited by 23
(2024) · doi:10.1109/icip51287.2024.10647307 · Cited by 18
Metadata
- Authors
- Ma, J., Zhang, Y., Gu, S., Zhu, C., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., Cao, S., Zhang, Q., Liu, S., Wang, Y., Li, Y., He, J., & Yang, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-10-01
- Volume
- 44
- Issue
- 10
- Pages
- 6695-6714
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3183302989
- Cited by
- 426
- References
- 96
- FWCI
- 20.64
- Type
- journal article
- Language
- en
- Open access
- green
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